Static Fairness-Aware Learning:
Static or batch fairness-aware learning approaches have received a lot of attention over the recent years. Literature for bias mitigation in this area can be categorized into: i) pre-processing, ii) in-processing and iii) post-processing approaches.
Pre-processing approaches [1]}, [2]}, [3]} focus on the data and aim to produce a “balanced” dataset that
can then be fed into any learning algorithm. Different “balancing” techniques have been proposed from label swapping, known as massaging [4]}, to increasing the representativeness of the protected group either via sampling [3]} or via (semi-)synthetic data augmentation [2]} and transforming the feature space to remove attribute correlations with the protected attribute [1]}.
In-processing approaches reformulate the classification problem by explicitly incorporating the model's discrimination
behavior in the objective function through regularization or constraints, or by training on latent target labels [8]}, [9]}. Post-processing approaches [10]}, [11]}, [12]} alter a model's predictions or adjust a model's decision boundary to reduce unfairness.
